We propose a novel technique for improving the efficiency of cloud-based continuous integration development environments. Our technique identifies repetitive, expensive and time-consuming setup activities that are required to run integration and system tests in the cloud, and consolidates them into preconfigured testing virtual machines such that the overall costs of test execution are minimized. We create such testing machines by reconfiguring and opportunistically snapshotting the virtual machines already registered in the cloud.

Contemporary cloud providers offer out-of-the-box auto-scaling solutions. However, defining a non-trivial scaling behavior that goes beyond the feature set provided by existing solutions is still challenging. In this paper we present SPEEDL, a declarative and extensible domain-specific language that simplifies the creation of elastic scaling behavior on top of IaaS clouds. SPEEDL simplifies the creation of event-driven policies for resource management (How many resources, and what resource types, are needed?), as well as task mapping (Which tasks should be handled by which resources?). Based on a dataset of real-life scaling policies, we demonstrate that SPEEDL can cover most scaling behaviors real-life developers want to express, and that the resulting SPEEDL policies are at the same time substantially more compact, easier to read, and less error-prone than the same behavior expressed via a general-purpose programming language.

Building Infrastructure-as-a-Service (IaaS) applications today is a complex, repetitive, and error-prone endeavor, as IaaS does not provide abstractions on top of virtual machines. This article presents JC loud S cale , a Java-based middleware for moving elastic applications to IaaS clouds, with minimal adjustments to the application code. We discuss the architecture and technical features, as well as evaluate our system with regard to user acceptance and performance overhead. Our user study reveals that JC loud S cale allows many participants to build IaaS applications more efficiently, compared to industrial Platform-as-a-Service (PaaS) solutions. Additionally, unlike PaaS, JC loud S cale does not lead to a control loss and vendor lock-in.

Many query languages for graph-structured data are based on regular path expressions, which describe relations among pairs of nodes. We propose an extension that allows to retrieve groups of nodes based on group structural characteristics and relations to other nodes or groups. It allows to express group selection queries in a concise and natural style, and can be integrated into any query language based on regular path queries. We present an efficient algorithm for evaluating group queries in polynomial time from an input data graph. Evaluations using real-world social networks demonstrate the practical feasibility of our approach.